LabelImg is now part of the Label Studio community. The popular image annotation tool created by Tzutalin is no longer actively being developed, but you can check out Label Studio, the open source data labeling tool for images, text, hypertext, audio, video and time-series data.

PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNet-V3/V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more

Annotate better with CVAT, the industry-leading data engine for machine learning. Used and trusted by teams at any scale, for data of any scale.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

Pretrained ConvNets for pytorch: NASNet, ResNeXt, ResNet, InceptionV4, InceptionResnetV2, Xception, DPN, etc.

Deep Learning and Reinforcement Learning Library for Scientists and Engineers

Segmentation models with pretrained backbones. PyTorch.

A PyTorch implementation of EfficientNet

Friendly machine learning for the web! 🤖

Caffe Implementation of Google's MobileNets (v1 and v2)

Efficient AI Backbones including GhostNet, TNT and MLP, developed by Huawei Noah's Ark Lab.

Caffe models (including classification, detection and segmentation) and deploy files for famouse networks

Classification with PyTorch.

Sandbox for training deep learning networks

Implementation of EfficientNet model. Keras and TensorFlow Keras.

DAMO-YOLO: a fast and accurate object detection method with some new techs, including NAS backbones, efficient RepGFPN, ZeroHead, AlignedOTA, and distillation enhancement.

Classification models trained on ImageNet. Keras.

PyTorch implementation of image classification models for CIFAR-10/CIFAR-100/MNIST/FashionMNIST/Kuzushiji-MNIST/ImageNet

DenseNet Caffe Models, converted from https://github.com/liuzhuang13/DenseNet

We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study